Fridays Academy: Gender and Economic Growth

At the outset, we expect a simultaneous relationship between gender inequality and economic growth. Stotsky (2006) states that “gender disparities lead to weaker economic growth and that stronger economic growth leads to reduced gender disparities” (p. 17). “Growth may affect gender inequalities by breaking down barriers to women’s work participation, by reducing the time spent in the home on non-market labor, and by changing institutional mores. We will examine basic statistical evidence, followed by theoretical evidence and finally empirical studies that develop a model that includes growth and gender equality.

First we note that a number of measures of gender equality are positively correlated with economic growth, as measured by income per capita. While this relationship does not imply causality, it suggests variables that may be used in regression analysis to determine the presence or absence of a statistically significant relationship that in turn may be used to inform policy. Stotsky (2006) plots the UN Gender Development Index (GDI) against the log of per capita income for 41 countries chosen randomly from the sample of IMF member countries and also between the Gender Equality Measure (GEM) and the log of per capita income for the 41 countries. Both graphs show positive and nonlinear relationships between the two indices and income suggesting that increasing income encourages greater gender equality in economic terms (as measured by the GDI) and political terms (as measured by the GEM). However, and as cautioned by Stotsky (2006), the methodology underlying these indices may play a part in the relationship.

A number of studies have examined the empirical relationship between alternative measures of gender equality based on a single indicator and income per capita. Stotsky (2006) examines the relationship between the ratio of females to males in primary education, the ratio in secondary education and the ratio in life expectancy. The table below summarizes the relationships.

Summary of Statistical Relationships and Indicators of Gender Equality

Source: Compiled from Stotsky (2006); p. 22

Stotsky (2006) concludes by noting that the relationship between single indicators of gender equality and income per capita is less clear compared to the indices such as GEM and GDI.Dollar and Gatti (1999) also examine correlations among gender measures and per capita income using data from 80 countries. The table below shows the results.

Correlations between gender measures and per capita income – 80 countries.

Source:Dollar and Gatti (1999); p. 26

The table shows that all measures of gender equality are positively correlated with per capita income, ranging from 28 percent for gender differences in secondary education to around 60 percent for female life expectancy minus male life expectancy, women’s economic rights (equal pay for equal work) and women’s rights within marriage.

Dividing countries based on 1990 per capita income according to the poorest and richest quartile shows even more striking differences, see table below. Beginning with the second of the education variables (see note at end of the table), just 5.4 percent of females in the lowest quartile have some higher education compared with 11.6 percent of males. The contrast with the richest quartile is even starker, where gender difference has been largely eliminated – 50.8 percent of women and 57.9 percent of men with some higher education. Differences in life expectancy between men and women is relatively little in the poorest quartile, roughly 3-years on average compared to 6-years in richer countries. According to a scale based on a range from 1 to 4 for different aspects of rights derived by Humana (1992) and quoted in Dollar and Gatti (1999), economic rights for women in poorest countries averages a 2 compared to 2.9 in the richest countries. Women’s rights within marriage average 2.3 in poorest countries compared to 3.6 in rich countries. Further difference can be seen with regard to the proportion of parliament seats held by women – averaging just 7 percent in poor countries compared to 17 percent in rich counties. Finally, almost 40 years had elapsed between the median year that women in the poorest countries were allowed to exercise the franchise and the median year for the richer countries (Dollar and Gatti, 1999).

Gender measures for the Poorest and Richest of Quintiles of Countries - 1990

* There are two education variables – secondary attainment is the share of the adult population for whom some secondary education is the highest level of attainment; superior attainment adds to secondary attainment the share of the population for whom some higher education is the highest level of attainment